The OffensEval task [1]} focuses on
identifying offensive language
in tweets, and determining if specific individuals or
groups are being targeted. Our approach was to rely
on traditional Machine Learning methods as implemented by
Scikit [2]} to build classifiers from
manually labeled examples of offensive tweets. Our models
relied on lexical features, including
character ngrams, words, and word ngrams. We also included
a dictionary based black–list approach, and experimented
with combining training data from two related yet different
SemEval-2019 tasks.
